Research on Intelligent Prediction of Surface Roughness in Cutting 42CrMo Steel by using Particle Swarm Optimization-based Support Vector Machine

Zhao, HaiYue; Cao, Yan; Sergey, Gorbachev; Kuzin, Victor; Du, Jiang; He, WeiLiang

doi:10.1007/s12541-024-01077-6

Int. J. Precis. Eng. Manuf.

2024

DOI: 10.1007/s12541-024-01077-6

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Research on Intelligent Prediction of Surface Roughness in Cutting 42CrMo Steel by using Particle Swarm Optimization-based Support Vector Machine

HaiYue Zhao,

Yan Cao,

Gorbachev Sergey

et al.

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2024

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Cited by 4 publications

References 54 publications

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Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning

Ziyad,

Alemayehu,

Wogaso

et al. 2024

Int J Adv Manuf Technol

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Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning

Ziyad,

Alemayehu,

Wogaso

et al. 2024

Int J Adv Manuf Technol

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Research on numerical simulation and prediction of tool wear in cutting ultra-high-strength aluminum alloys

Zhao,

Cao,

Gorbachev

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Image-processing-based model for surface roughness evaluation in titanium based alloys using dual tree complex wavelet transform and radial basis function neural networks

Vishwanatha,

Srinivasa Pai,

D’Mello

et al. 2024

Sci Rep

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In this study, we examine the assessment of surface roughness on turned surfaces of Ti 6Al 4V using a computer vision system. We utilize the Dual-Tree Complex Wavelet Transform (DTCWT) to break down the images of the turned surface into sub-images oriented in directions. Three different methods of feature generation have been compared, i.e., the use of Gray-Level Co-Occurrence Matrix (GLCM) and DTCWT-based extraction of second-order statistical features, DTCWT Image fusion, and the use of GLCM for feature extraction, and DTCWT image fusion using Particle Swarm Optimization (PSO) based GLCM features. Principal Component Analysis (PCA) was utilized to identify and select features. The model was developed using a Radial Basis Function Neural Network (RBFNN). Accordingly, six models were designed based on the three feature generation methods, considering all features and features selected using PCA. The RBFNN model, which incorporates DTCWT Image fusion and utilizes PSO with PCA features, achieved a training data prediction accuracy of 100% and a test data prediction accuracy of 99.13%.

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Research on Intelligent Prediction of Surface Roughness in Cutting 42CrMo Steel by using Particle Swarm Optimization-based Support Vector Machine

Cited by 4 publications

References 54 publications

Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning

Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning

Research on numerical simulation and prediction of tool wear in cutting ultra-high-strength aluminum alloys

Image-processing-based model for surface roughness evaluation in titanium based alloys using dual tree complex wavelet transform and radial basis function neural networks

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